Sequential Anomaly Detection
Sequential Anomaly Detection is a computational framework within sequence pattern recognition that identifies deviations from expected sequential behaviors by modeling legitimate sequences and detecting instances where observed transitions or ordering of elements significantly diverge from established temporal or structural norms.
118
Publications
5.5K
Citations
391
Authors
179
Institutions
Publications per year
2017–2026
74
Parents
118
LogAnomaly: Unsupervised Detection of Sequential and Quantitative Anomalies in Unstructured Logs
Weibin Meng, Ying Liu, Yichen Zhu et al. · 2019 · 616 citations · Full text
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University of Illinois Urbana-Champaign
Urbana, United States
11 h-index
17 publications
807 citations
Ben-Gurion University of the Negev
Beersheba, Israel
8 h-index
17 publications
415 citations

Cornell University
Ithaca, United States
8 h-index
12 publications
335 citations

Bilkent University
Ankara, Türkiye
5 h-index
10 publications
429 citations
Tsinghua University
Beijing, China
4 h-index
12 publications
3.6K citations
University of California, Davis
Davis, United States
4 h-index
7 publications
275 citations
Duke University
Durham, United States
4 h-index
8 publications
210 citations
University of South Florida
Tampa, United States
3 h-index
7 publications
124 citations
1–8 of 179
5 publications · 252 citations
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5 publications · 185 citations
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2 publications · 36 citations
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TON · 2 publications · 114 citations
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2 publications · 233 citations
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